Two years ago I moved to Dartmouth College (Department of Biomedical Data Science) from MD Anderson Cancer Center (MDACC) University of Texas. At Dartmouth I continue the research I was involved at MDACC.
My research interest is in identification and understanding genetic factors influencing cancer risk and progression. We demonstrated that a joint analysis of GWAS and gene expression data improves prediction of the genes associated with cancer risk. Recently in collaboration with Dr. Amos and others, I developed an empirical model to identify disease-associated SNPs that are likely to be independently validated. The model predicts SNP reproducibility as good as P-values from the discovery study even though the model does not use any GWAS-derived data for the prediction. My current research activity can be divided into (1) development of novel bioinformatics approaches to identify genetic factors influencing cancer risk and progression, and (2) application of the approaches to the real data. I published a number of papers on using gene expression data to identify genes associated with cancer initiation and development. I worked and am continuing to work on the development of better algorithms using gene expression data for better understanding cancer biology. We conducted a number of studies on meta-analysis of the gene expression data with a goal to identify novel candidate genes and understand cancer biology. We developed a number of analytical approaches to better predict cancer genes from gene expression data. Currently we are working on developing a method that will allow ranking all human genes by their cancer relevance, and this will help with interpretation of clinical relevance of somatic mutations and identification of novel cancer genes.